DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/05/2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The Amendment filed 03/05/2026 has been entered. Claims 1-20 remain pending in the application. The U.S.C. 101 rejection is withdrawn.
Response to Arguments
Applicant's arguments filed 03/05/2026, in respect to the 35 U.S.C. 103 rejection, have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 101 rejection, on pages 10-15, the applicant has amended their claim to overcome the rejection. This rejection is hereby withdrawn.
With respect to the 35 U.S.C. 103 rejection of claims 1, 3-6, 8-9, 12-14, 17, and 19-20 under Somaiya et al. (US Patent Application Publication No. 2016/0078101), hereinafter referred to as Somaiya, in view of Bodigutla (US Patent Application Publication No. 2023/0140702), claims 2, 10-11, 15-16, and 18 further in view of Goyal et al. (US Patent Application Publication No. 2017/0161373), hereinafter referred to as Goyal, and claim 7 further in view of Lu et al. (US Patent Application Publication No. 2011/0258212), hereinafter Lu, the Applicant asserts that the cited art fails to teach or suggest the amended claims. The Applicant asserts that neither Somaiya nor Bodigutla discloses “generating, by a trained deep learning model, one or more model candidate queries based on the one or more heuristic candidate queries, wherein the deep learning model has been trained, using one or more gathered user queries, to automatically recognize a language patter and determine one or more potential model candidate queries” and “transmitting, by the one or more processors, the selected at least one candidate query to at least one device for display on the at least one device” with respect to claim 1. The Applicant further asserts specifically that Somaiya does not disclose any training features, including those limitations listed above, nor displaying on a device. The Applicant also asserts that Bodigutla specifically does not disclose “wherein the deep learning model has been trained, using one or more gathered user queries, to automatically recognize a language patter and determine one or more potential model candidate queries” as recited in the amended independent claims.
In response to Somaiya, in view of Bodigutla, not disclosing or suggesting “generating, by a trained deep learning model, one or more model candidate queries based on the one or more heuristic candidate queries, wherein the deep learning model has been trained, using one or more gathered user queries, to automatically recognize a language pattern and determine one or more potential model candidate queries”, Bodigutla para [0015] states "To recommend quality search queries, a deep reinforcement learning model is created to predict the query a user would enter next," and Bodigutla para [0017] states "One general aspect includes obtaining a supervised model by training a machine-learning (ML) program with training data that includes search queries entered by users of an online service." The above statements from Bodigutla teach the training of a deep learning model on search queries from users of an online service (i.e., gathered search queries) and its subsequent usage of this trained model to recommend search queries related to those being input into the system (i.e., candidate queries). Deep learning models and machine learning models, specifically those used with natural language, inherently determine language patterns as they are trained, as this is the purpose of training the models. It would have been obvious to have modified Somaiya’s disclose of determining query suggestions by incorporating Bodigutla’s teaching of utilizing a trained deep learning model, as this would enhance the speed and accuracy of the system.
In response to Somaiya, in view of Bodigutla, not disclosing or suggesting “transmitting, by the one or more processors, the selected at least one candidate query to at least one device for display on the at least one device”, Somaiya para [0022] states "In some embodiments, the client device 110 comprises a display module (not shown) to display information (e.g., in the form of user interfaces)," and Somaiya para [0043] further states “In some embodiments, the presentation module 280 can cause presentation of the combined search query suggestions by transmitting the combined search query suggestions to the client device 110.” This directly shows that Somaiya utilizes a user, or client, device to present the suggested queries to the user through transmitting the suggested queries to the client device, thus disclosing the amended limitations.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3-6, 8-9, 12-14, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somaiya et al. (US Patent Application Publication No. 2016/0078101), hereinafter referred to as Somaiya, in view of Bodigutla (US Patent Application Publication No. 2023/0140702).
Regarding claim 1, Somaiya discloses a computer-implemented method for using heuristic refinement and a deep learning model to identify at least one high-quality user search query ("the selection module 260 of the search enhancement system 150 employs a filter function to select qualified query items for inclusion," Somaiya para [0069]), the method comprising: filtering, by one or more processors, one or more queries based on one or more user interactions ("The global token pool may be understood to be a set of tokens, token portions, query portions, or search queries used by a set of users which interact with the search enhancement system 150," Somaiya para [0037]);
("In some example embodiments, the search enhancement system 150 employs an n-gram model to reduce workload on the server, by eliminating unlikely candidate queries. The n-gram model may operate as an optimization heuristic reducing workload on the servers described herein and improving response time of the search enhancement system 150," Somaiya para [0082] AND "As discussed above, features for historical tokens may include usage time of the token, semantic relation of the token to other tokens or query items, a query time, and other suitable features which may indicate relative placement of tokens or query items within the token pool and relationships between tokens or query items within the token pool," Somaiya para [0089]);
and selecting, by the one or more processors, [[ the ]] at least one candidate query from the one or more heuristic candidate queries or the one or more model candidate queries based on a corresponding aggregated token frequency (Somaiya Fig. 3 reference character 330, 340, and 350 and "Token features may also include token frequency, token recency, and other features recordable when the token or query item is received into the token pool," Somaiya para [0056]);
and transmitting, by the one or more processors, the selected at least one candidate query [[ on ]] to at least one device for display on the at least one device ("In some embodiments, the client device 110 comprises a display module (not shown) to display information (e.g., in the form of user interfaces)," Somaiya para [0022] and “In some embodiments, the presentation module 280 can cause presentation of the combined search query suggestions by transmitting the combined search query suggestions to the client device 110,” Somaiya para [0043]).
However, Somaiya does not disclose generating, by a trained deep learning model, one or more model candidate queries based on the one or more heuristic candidate queries, wherein the deep learning model has been trained, using one or more gathered user queries, to automatically recognize a language pattern and determine one or more potential model candidate queries. Bodigutla teaches a method for suggesting related search queries through reinforcement learning.
Bodigutla teaches generating, by a trained deep learning model, one or more model candidate queries based on the one or more heuristic candidate queries ("To recommend quality search queries, a deep reinforcement learning model is created to predict the query a user would enter next," Bodigutla para [0015]), wherein the deep learning model has been trained, using one or more gather user queries, to automatically recognize a language pattern and determine one or more potential model candidate queries ("One general aspect includes obtaining a supervised model by training a machine-learning (ML) program with training data that includes search queries entered by users of an online service," Bodigutla para [0017]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Somaiya’s method of enhancing search query suggestions by including Bodigutla’s usage of deep learning to recommend search queries. Deep learning excels in natural language processing and has an ability to learn complex patterns efficiently. This would allow for a better quality of queries to be discovered. This inclusion of deep learning would have been obvious to one of ordinary skill in the art.
Regarding claim 3, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya further discloses wherein the one or more heuristic processes includes at least one of: filtering the filtered one or more queries based on at least one query length, filtering the filtered one or more queries based on at least one popularity score, or applying at least one smoothing strategy to the filtered one or more queries ("Each history query is indexed and scored based on a popularity calculation model," Somaiya para [0019]).
Regarding claim 4, Somaiya, in view of Bodigutla, discloses all the limitations of claim 3. Somaiya further discloses wherein the at least one popularity score includes a click-based popularity score, a view-based popularity score, or a Wilson-based popularity score ("Each history query is indexed and scored based on a popularity calculation model, part of a global scoring function. Frequency and other dimensions are incorporated in the popularity calculation," Somaiya para [0019]).
Regarding claim 5, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya further discloses wherein generating the one or more model candidate queries based on the one or more deep learning model processes further comprises: receiving, by the one or more processors, term data from one or more external sources ("The receiver module 210 receives a query portion from the client device 110," Somaiya para [0036]);
concatenating, by the one or more processors, the term data ("The token data represents a search query or represents tokens which may be combined with other tokens to form a search query," Somaiya para [0036]);
extracting, by the one or more processors, one or more tokens from the term data ("The query portion includes at least a token portion," Somaiya para [0036]);
and applying, by the one or more processors, at least one language model to the one or more tokens to determine the one or more model candidate queries ("In some example embodiments, the search enhancement system 150 employs an n-gram model to reduce workload on the server," Somaiya para [0082]).
Regarding claim 6, Somaiya, in view of Bodigutla, discloses all the limitations of claim 5. Somaiya further discloses determining, by the one or more processors, the aggregated token frequency for each of the one or more heuristic candidate queries and each of the one or more model candidate queries, the aggregated token frequency based on the term data and a frequency of the one or more heuristic candidate queries or the one or more model candidate queries in the term data ("Token frequency is a metric used to describe how prominently a specific token appears in the session history, according to various embodiments. Token frequency may be determined using a multitude of strategies. For example, the token frequency is determined as the frequency of a specific token in the session history," Somaiya para [0056]);
and selecting, by the one or more processors, the one or more heuristic candidate queries and each of the one or more model candidate queries with a highest aggregated token frequency as the at least one candidate query ("the search enhancement system sorts all bigrams by frequencies in the bigram model, picks the bigram with the ranking l %*k as a pivot, and selects all bigrams with frequencies greater than or equal to the pivot," Somaiya para [0084]).
Regarding claim 8, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya further discloses receiving, by the one or more processors, the one or more queries from one or more devices ("The receiver module 210 receives a query portion from the client device 110," Somaiya para [0036]).
Regarding claim 9, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya further discloses wherein the one or more queries include a plurality of terms ("Search query suggestions often represent the search system determining suggested terms as related to the terms or partial terms input into the search query," Somaiya para [0003]).
As to claim 12, system claim 12 and method claim 1 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 12 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 13, system claim 13 and method claim 5 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 13 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 14, system claim 14 and method claim 6 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 14 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 17, computer-readable medium (CRM) claim 17 and method claim 1 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 17 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 19, CRM claim 19 and method claim 3 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 19 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 20, CRM claim 20 and method claim 4 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 20 is similarly rejected under the same rationale as applied above with respect to the method claim.
Claim(s) 2, 10-11, 15-16, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somaiya, in view of Bodigutla, and further in view of Goyal et al. (US Patent Application Publication No. 2017/0161373), hereinafter referred to as Goyal.
Regarding claim 2, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya fails to disclose wherein the one or more user interactions include one or more clicks. Goyal teaches a method for providing context based query suggestions.
Goyal teaches wherein the one or more user interactions include one or more clicks ("In addition, from the query logs in the query log database 150, the search suggestion engine 140 may also cluster similar queries together based on their most clicked URLs," Goyal para [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Somaiya’s method of enhancing search query suggestions by including Goyal’s usage of counting clicks. Using the amount of user interactions, such as clicks, a particular query receives would allow for a better ranking and query suggestion system. This is because the number of times an item or query has been interacted with by other users makes it statistically more likely that the current user will choose this recommended query. This inclusion of click counting would have been obvious to one of ordinary skill in the art.
Regarding claim 10, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya fails to disclose clustering, by the one or more processors, the filtered one or more queries into one or more query groups based on one or more similarities.
Goyal teaches clustering, by the one or more processors, the filtered one or more queries into one or more query groups based on one or more similarities (Goyal Fig. 5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Somaiya’s method of enhancing search query suggestions by including Goyal’s usage of clustering queries by similarity. Recommending queries that are similar to the input query would result in a better query suggestion system. Grouping queries together based on similarity, i.e. by popularity, word usage, token count, etc., results in a group of closely related queries that could be more similar to what the user was attempting to search. This inclusion of grouping queries by similarity would have been obvious to one of ordinary skill in the art.
Regarding claim 11, Somaiya, in view of Bodigutla, and further in view of Goyal, discloses all the limitations of claim 10. Somaiya further discloses wherein the selecting further comprises: assigning, by the one or more processors, a popularity score to each filtered one or more queries in the one or more query groups ("Each history query is indexed and scored based on a popularity calculation model," Somaiya para [0019]);
and selecting, by the one or more processors, based on the popularity score, at least one popular query for each of the one or more query groups ("Each history query is indexed and scored based on a popularity calculation model, part of a global scoring function. Frequency and other dimensions are incorporated in the popularity calculation," Somaiya para [0019]).
As to claim 15, system claim 15 and method claim 10 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 15 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 16, system claim 16 and method claim 11 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 16 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 18, CRM claim 18 and method claim 2 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly, claim 18 is similarly rejected under the same rationale as applied above with respect to the method claim.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Somaiya, in view of Bodigutla, and further in view of Lu et al. (US Patent Application Publication No. 2011/0258212), hereinafter Lu.
Regarding claim 7, Somaiya, in view of Bodigutla, discloses all the limitations of claim 1. Somaiya fails to disclose wherein a query length of the at least one candidate query is below a threshold. Lu teaches a method for query suggestions using sub-queries.
Lu teaches wherein a query length of the at least one candidate query is below a threshold ("The query length can be about 75 query elements or less, or about 60 query elements or less, or about 50 query elements or less, or about 40 query elements or less," Lu para [0019]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Somaiya’s method of enhancing search query suggestions by including Lu’s usage of having a query length below a threshold. “However, as a query increases in length, the number of variations can increase exponentially, leading to a large number of permutations that are evaluated in order to determine the query suggestions,” Lu para [0014]. Keeping a query length below a certain threshold would be beneficial, as it would decrease computational effort needed, thereby increasing efficiency of suggesting alike queries. This inclusion of only using a query length below a threshold would have been obvious to one of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent Application Publication No. 2021/0149963
US Patent Application Publication No. 2020/0349634
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/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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